Vertex AI API

ai-ml google

Vertex AI API

Google Cloud’s unified ML platform for training, deploying, and serving models.

Quick Facts

FieldValue
ProviderGoogle
CategoryAI & Machine Learning APIs
Websitehttps://cloud.google.com/vertex-ai
AuthenticationOAuth 2.0
Pricing Modelusage-based
Free TierLimited free credits for new users
Rate Limit60 req/min (varies by model)

Overview

Vertex AI API is a ai & machine learning apis provided by Google. Google Cloud’s unified ML platform for training, deploying, and serving models. This API is designed to help developers integrate ai-ml capabilities into their applications with minimal setup and maximum reliability.

The API supports OAuth 2.0 authentication, ensuring secure access to all endpoints. With a usage-based pricing model, Google offers flexible options for projects of any size, from prototypes to enterprise deployments.

Google maintains comprehensive documentation, SDKs for popular programming languages, and active community support. The API is built on REST principles, returning JSON responses with standard HTTP status codes, making it straightforward to integrate into existing workflows.

Authentication

This API uses OAuth 2.0 authentication. Exchange your client credentials for an access token, then send the token as a Bearer token in the Authorization header.

Always store your credentials securely using environment variables or a secrets manager. Never commit API keys to version control or expose them in client-side code.

Code Samples

Python

import requests
import os

API_KEY = os.environ.get("API_KEY", "YOUR_API_KEY")
BASE_URL = "https://googleapis.com"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

response = requests.get(f"{BASE_URL}/v1/resources", headers=headers)
print(response.status_code)
print(response.json())

JavaScript

const API_KEY = process.env.API_KEY || 'YOUR_API_KEY';
const BASE_URL = 'https://googleapis.com';

const response = await fetch(`${BASE_URL}/v1/resources`, {
  headers: {
    'Authorization': `Bearer ${API_KEY}`,
    'Content-Type': 'application/json'
  }
});
const data = await response.json();
console.log(data);

cURL

curl -H "Authorization: Bearer $API_KEY" \
     -H "Content-Type: application/json" \
     https://googleapis.com/v1/resources

Go

package main

import (
    "net/http"
    "fmt"
    "io"
    "os"
)

func main() {
    apiKey := os.Getenv("API_KEY")
    if apiKey == "" {
        apiKey = "YOUR_API_KEY"
    }
    req, _ := http.NewRequest("GET", "https://googleapis.com/v1/resources", nil)
    req.Header.Set("Authorization", "Bearer " + apiKey)
    req.Header.Set("Content-Type", "application/json")
    resp, _ := http.DefaultClient.Do(req)
    defer resp.Body.Close()
    body, _ := io.ReadAll(resp.Body)
    fmt.Println(string(body))
}

Pricing

The pricing model is usage-based. Pay only for what you use, with pricing based on API calls, tokens, or compute time. Visit the provider’s pricing page at https://cloud.google.com/vertex-ai for current rates and detailed pricing information.

Use Cases

  • Integration: Connect Google services to your application for seamless ai-ml functionality.
  • Automation: Automate ai-ml workflows and reduce manual operations.
  • Scaling: Handle growing ai-ml demands with Google’s robust infrastructure.

Further Reading

Other languages